Abstract
Disease classification based on gene information has been of significance as the foundation for achieving precision medicine. Previous works focus on classifying diseases according to the gene expression data of patient samples, and constructing disease network based on the overlap of disease genes, as many genes have been confirmed to be associated with diseases. In this work, the effects of diseases on human biological functions are assessed from the perspective of gene network modules and pathways, and the distances between diseases are defined to carry out the classification models. In total, 1728 diseases are divided into 12 and 14 categories by the intensity and scope of effects on pathways, respectively. Each category is a mix of several types of diseases identified based on congenital and acquired factors as well as diseased tissues and organs. The disease classification models on the basis of gene network are parallel with traditional pathology classification based on anatomic and clinical manifestations, and enable us to look at diseases in the viewpoint of commonalities in etiology and pathology. Our models provide a foundation for exploring combination therapy of diseases, which in turn may inform strategies for future gene-targeted therapy.
Highlights
Characterizing disease in the biological big data era of the twentyfirst century has been of significance [1], based on pathological analysis and clinical syndromes and molecularlevel information, including gene data
Each category is a mix of several types of diseases identified based on congenital and acquired factors as well as diseased tissues and organs, which implies that the human gene network gives a perspective of disease classifications, and guides future gene-targeted therapy and combination therapy of diseases
As each module has a different number of genes, we apply the adjusted cosine similarity to measure the correlations of pathway genes distribution in network modules
Summary
Characterizing disease in the biological big data era of the twentyfirst century has been of significance [1], based on pathological analysis and clinical syndromes and molecularlevel information, including gene data. Many network-based approaches are proposed in order to explore the associations between human genetic diseases and the relations between their effector genes [22,23,24,25]. Lee et al [24] construct a bipartite human disease association network where the disease pairs are connected if mutated enzymes associated with them catalyse adjacent metabolic reactions This network topology-based approach helps to uncover potential mechanisms that contribute to their shared pathophysiology [24]. Li et al take a comprehensive perspective on oncogenic processes based on Pan-Cancer Atlas analyses, giving prominence to the complex impact of genome alterations on the signalling and multi-omic profiles of human cancers as well as their influence on tumour microenvironment [9]. Each category is a mix of several types of diseases identified based on congenital and acquired factors as well as diseased tissues and organs, which implies that the human gene network gives a perspective of disease classifications, and guides future gene-targeted therapy and combination therapy of diseases
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